The present invention relates to methods and systems for estimating changes in an anatomical structure over time.
During the last several years, there has been an increased emphasis on longitudinal analysis in clinical studies. Specifically, longitudinal analysis has led to advances in the understanding of developmental disabilities such as autism and neurodegenerative diseases such as Huntington's disease. The framework for both studies is quite similar: clinically relevant measurements are extracted from imaging data and a continuous evolution is estimated by fitting a regression model to the discrete measures. Subsequent statistical analysis is conducted using the trajectories estimated during regression.
Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a one-dimensional (1D) regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm.